Optimality driven nearest centroid classification from genomic data

Alan R. Dabney, John D. Storey

Research output: Contribution to journalArticlepeer-review

26 Scopus citations


Nearest-centroid classifiers have recently been successfully employed in high-dimensional applications, such as in genomics. A necessary step when building a classifier for high-dimensional data is feature selection. Feature selection is frequently carried out by computing univariate scores for each feature individually, without consideration for how a subset of features performs as a whole. We introduce a new feature selection approach for high-dimensional nearest centroid classifiers that instead is based on the theoretically optimal choice of a given number of features, which we determine directly here. This allows us to develop a new greedy algorithm to estimate this optimal nearest-centroid classifier with a given number of feetures. In addition, whereas the centroids; are usually formed from maximum likelihood estimates, we investigate the applicability of high-dimensional shrinkage estimates of centroids. We apply the proposed method to clinical classification based on gene-expression microarrays, demonstrating that the proposed method can outperform existing nearest centroid classifiers.

Original languageEnglish (US)
Article numbere1002
JournalPloS one
Issue number10
StatePublished - Oct 3 2007

All Science Journal Classification (ASJC) codes

  • General


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